{"id":"W2858166984","doi":"10.1117/12.2313924","title":"Gemini infrared multi-object spectrograph: instrument overview","year":2018,"lang":"en","type":"preprint","venue":"Ground-based and Airborne Instrumentation for Astronomy VII","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Université Laval; Saint Mary's University; University of Victoria; York University; University of British Columbia; University of Toronto; Dalhousie University; Herzberg Institute of Astrophysics","funders":"British Columbia Knowledge Development Fund","keywords":"Spectrograph; Telescope; Physics; Pathfinder; First light; Remote sensing; Angular resolution (graph drawing); Integral field spectrograph; Field of view; Adaptive optics; Instrumentation (computer programming); Computer science; Scientific instrument; Very Large Telescope; Astronomy; Optics; Galaxy; Light source; Geography; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003806422,0.0009989209,0.000946936,0.0003955483,0.0006136539,0.0006363177,0.0006261835,0.0002342548,0.000825514],"category_scores_gemma":[0.00001131122,0.001034404,0.0007425574,0.0002938255,0.0005699918,0.0004403607,0.0006250184,0.000875283,0.00006167567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002505409,"about_ca_system_score_gemma":0.0007672102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003614899,"about_ca_topic_score_gemma":0.0000102975,"domain_scores_codex":[0.9956651,0.0001566298,0.001014395,0.001461789,0.0005166293,0.001185452],"domain_scores_gemma":[0.9975753,0.0001666842,0.0007220298,0.0007767245,0.0002636138,0.0004956021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006228671,0.001317379,0.03641009,0.0003668566,0.001054347,0.000001664004,0.0002587243,0.0003961285,0.0002533083,0.007838725,0.00109855,0.9503813],"study_design_scores_gemma":[0.09323891,0.01689031,0.2884085,0.007051873,0.004395172,0.000008952097,0.01549241,0.0957969,0.1653988,0.0735102,0.2220512,0.01775668],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4707945,0.0001768835,0.5214846,0.0003878665,0.0007737719,0.003243341,0.001760079,0.0001618656,0.001217014],"genre_scores_gemma":[0.9054765,0.00001074937,0.08720324,0.0001000791,0.001427474,0.001619422,0.003694541,0.0001087694,0.0003592062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9326247,"threshold_uncertainty_score":0.9992107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03958054218055288,"score_gpt":0.3177515800349844,"score_spread":0.2781710378544315,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}